公开(公告)号：US10185713B1

公开(公告)日：2019-01-22

申请号：US14867932

申请日：2015-09-28

Applicant: Amazon Technologies, Inc.

Inventor： Michael Denkowski ,  Alon Lavie ,  Gregory Alan Hanneman ,  Austin Matthews ,  Matthew Ryan Fiorillo ,  Robert Thomas Olszewski ,  Christopher James Dyer ,  William Joseph Kaper ,  Alexandre Alexandrovich Klementiev ,  Gavin R. Jewell

IPC: G06F17/28 ,  G06F17/27

Abstract: Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.

公开(公告)号：US10679015B1

公开(公告)日：2020-06-09

申请号：US14981536

申请日：2015-12-28

Applicant: Amazon Technologies, Inc.

Inventor： Gyorgy Istvan Szarvas ,  Gregory Alan Hanneman ,  Alon Lavie

IPC: G06F40/00 ,  G06F40/58 ,  G06N7/00 ,  G06N20/00

Abstract: Technologies are disclosed for utilizing artificial intelligence-based machine translation to augment document summarization. Text can be extracted from a document in a first language. Machine translation can be utilized to translate the text from the first language to a second language. The translated text can be used to identify documents in the second language that include support for the translated text. A user interface can be provided that indicates the number of documents in the second language that provide support for the extracted text. Documents in the first language can also be translated to the second language. Documents that provide support for a text string can be identified in the documents translated to the second language and in other documents in the second language. A user interface can be provided that indicates the number of documents in the first language and the second language that provide support for the text.

公开(公告)号：US10437933B1

公开(公告)日：2019-10-08

申请号：US15238101

申请日：2016-08-16

Applicant: Amazon Technologies, Inc.

Inventor： Ann Clifton ,  Michael Denkowski ,  Alon Lavie

IPC: G06F17/28

Abstract: A machine translation system capable of clustering training data and performing dynamic domain adaptation is disclosed. An unsupervised domain clustering process is utilized to identify domains in general training data that can include in-domain training data and out-of-domain training data. Segments in the general training data are then assigned to the domains in order to create domain-specific training data. The domain-specific training data is then utilized to create domain-specific language models, domain-specific translation models, and domain-specific model weights for the domains. An input segment to be translated can be assigned to a domain at translation time. The domain-specific model weights for the assigned domain can be utilized to translate the input segment.

公开(公告)号：US10268684B1

公开(公告)日：2019-04-23

申请号：US14868166

申请日：2015-09-28

Applicant: Amazon Technologies, Inc.

Inventor： Michael Denkowski ,  Alon Lavie ,  Gregory Alan Hanneman ,  Matthew Ryan Fiorillo ,  Laura Josephine Kieras ,  Robert Thomas Olszewski ,  William Joseph Kaper ,  Alexandre Alexandrovich Klementiev ,  Gavin Richard Jewell

IPC: G06F17/28 ,  G06F17/24

Abstract: Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.

公开(公告)号：US09959271B1

公开(公告)日：2018-05-01

申请号：US14868083

申请日：2015-09-28

Applicant: Amazon Technologies, Inc.

Inventor： Kartik Goyal ,  Alon Lavie ,  Michael Denkowski ,  Gregory Alan Hanneman ,  Matthew Ryan Fiorillo ,  Robert Thomas Olszewski ,  Ehud Hershkovich ,  William Joseph Kaper ,  Alexandre Alexandrovich Klementiev ,  Gavin R. Jewell

IPC: G06F17/30 ,  G06F17/28 ,  G06F17/27 ,  G06F17/24

CPC classification number: G06F17/2818 ,  G06F17/2854

Abstract: Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.